Key result
Automated PPG algorithm detects systolic peaks with ~100% sensitivity against manual expert annotation.
Why the study?
Few algorithms are published for peak detection suitable for pulse wave signals such as photoplethysmographic signals, and ECG-based methods are not applicable due to different signal characteristics.
Answers are grounded in this paper and cite their sources.
Validation study demonstrates accurate systolic peak detection in photoplethysmographic signals, indicating strong potential for real-time hardware implementation.
Processing of physiological signals often involves detection of peaks and finding intervals between them.Well developed methods are available for Electrocardiogram(ECG) QRS complex detection.However, there are only a few algorithms published for peak detection suitable for pulse wave signals such as arterial pressure wave and photoplethysmographic (PPG) signals.Algorithms for detection of QRS complex in ECG are based on the impulsive character of the signal and are not applicable for pulse wave signals, which are more sinusoidal in nature and the shape varies with age.In this background, a versatile algorithm based on the physiology of the pulse wave is developed to detect the peaks from a pulse wave signal such as PPG.The algorithm combines the technique of moving average of valley-peak differences with an adaptive threshold filtering to detect the systolic peaks.The algorithm is validated against a publicly available validation dataset and achieved a sensitivity of 99.82 and a positive predictivity of 98.88 when compared to expert manual annotations.This algorithm is computationally simple and can be easily implemented in real time processing hardware.
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Kuntamalla et al. (2014) studied Healthy and cardiovascular/diabetic conditions (n=47). Automatic systolic peak detection algorithm vs. Expert manual annotations was evaluated on Sensitivity and positive predictivity for detecting systolic peaks in photoplethysmographic signals. The proposed automatic systolic peak detection algorithm for photoplethysmographic signals achieved a sensitivity of 99.82% and a positive predictivity of 98.88% against expert manual annotations.
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